LSOA domestic Electricty and Gas maps
Meghan Kinglsey-Walsh
Last run at: 2021-09-03 14:40:50
1 Background data
Load & check boundary data for Solent area. The table shows which LAs we expect to plot.
#The LSOA boundaries for the Solent have been pre-downloaded
inf<-paste0(dataFolder, "boundaries/LSOA/lsoa_solent.shp")
message ("Loading LSOA boundaries from file")## Loading LSOA boundaries from filelsoa_sf <- sf::read_sf(inf)
head(lsoa_sf)## Simple feature collection with 6 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 465991.4 ymin: 100849 xmax: 467770.5 ymax: 102360.8
## Projected CRS: OSGB 1936 / British National Grid
## # A tibble: 6 × 16
## LSOA11CD OBJECTID LSOA11NM LSOA11NMW BNG_E BNG_N LONG LAT Shape__Are
## <chr> <int> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 E01017013 16519 Portsmouth… Portsmout… 466127 101741 -1.06 50.8 145134.
## 2 E01017014 16520 Portsmouth… Portsmout… 466924 101957 -1.05 50.8 757371.
## 3 E01017015 16521 Portsmouth… Portsmout… 467084 101499 -1.05 50.8 472647.
## 4 E01017016 16522 Portsmouth… Portsmout… 466330 101597 -1.06 50.8 122927.
## 5 E01017017 16523 Portsmouth… Portsmout… 466813 101069 -1.05 50.8 166924.
## 6 E01017018 16524 Portsmouth… Portsmout… 466350 101184 -1.06 50.8 184945.
## # … with 7 more variables: Shape__Len <dbl>, MSOA11CD <chr>, MSOA11NM <chr>,
## # LAD11CD <chr>, LAD11NM <chr>, nLSOAs <int>, geometry <MULTIPOLYGON [m]>table(lsoa_sf$LAD11NM) # How many LSOAs are there in each LA?##
## Basingstoke and Deane East Hampshire Eastleigh
## 109 72 77
## Fareham Gosport Hart
## 73 53 57
## Havant Isle of Wight New Forest
## 78 89 114
## Portsmouth Southampton Test Valley
## 125 148 71
## Winchester
## 70st_coord_sys <- st_crs(lsoa_sf)
st_coord_sys$epsg## [1] 27700#Trasforming the coord systen
if(st_coord_sys$epsg !=4326){
lsoa_sf_leaflet <- st_transform(lsoa_sf, "+proj=longlat +datum=WGS84")
}Loading deprivation data and double checking what the IMD Decile means - NB this is all LSOAs not just Solent
inFile<-paste0(dataFolder, "Indices of Multiple Deprivation/Indices_of_Multiple_Deprivation_(IMD)_2019.csv")
LSOAdep<-readr::read_csv(inFile)## Rows: 32844 Columns: 66## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): lsoa11cd, lsoa11nm, lsoa11nmw, LSOA01NM, LADcd, LADnm
## dbl (60): FID, st_areasha, st_lengths, IMD_Rank, IMD_Decile, IMDScore, IMDRa...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.LSOAdep$'LSOA11CD' <- LSOAdep$'lsoa11cd'
head(LSOAdep)## # A tibble: 6 × 67
## FID lsoa11cd lsoa11nm lsoa11nmw st_areasha st_lengths IMD_Rank IMD_Decile
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 E01000816 Bromley 040A Bromley … 299021. 3337. 19874 7
## 2 2 E01000818 Bromley 040B Bromley … 1292309. 7408. 20548 7
## 3 3 E01000819 Bromley 040C Bromley … 397713. 4384. 9866 4
## 4 4 E01000820 Bromley 008C Bromley … 587458. 4838. 13461 5
## 5 5 E01000822 Bromley 008D Bromley … 218524. 2738. 20757 7
## 6 6 E01000823 Bromley 008E Bromley … 242692. 3195. 22675 7
## # … with 59 more variables: LSOA01NM <chr>, LADcd <chr>, LADnm <chr>,
## # IMDScore <dbl>, IMDRank0 <dbl>, IMDDec0 <dbl>, IncScore <dbl>,
## # IncRank <dbl>, IncDec <dbl>, EmpScore <dbl>, EmpRank <dbl>, EmpDec <dbl>,
## # EduScore <dbl>, EduRank <dbl>, EduDec <dbl>, HDDScore <dbl>, HDDRank <dbl>,
## # HDDDec <dbl>, CriScore <dbl>, CriRank <dbl>, CriDec <dbl>, BHSScore <dbl>,
## # BHSRank <dbl>, BHSDec <dbl>, EnvScore <dbl>, EnvRank <dbl>, EnvDec <dbl>,
## # IDCScore <dbl>, IDCRank <dbl>, IDCDec <dbl>, IDOScore <dbl>, …#names(LSOAdep)
ggplot(LSOAdep, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y = IMDScore,
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
labs(x = "IMD Decile",
y = "IMD Score",
caption = "All LSOAs")
LSOAdep$la_name <- LSOAdep$LADnm
solent_dep <- getSolent(LSOAdep)
ggplot(solent_dep, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y = IMDScore,
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
facet_wrap(. ~ la_name)
labs(x = "IMD Decile",
y = "IMD Score",
caption = "Solent LSOAs split by Local Authority")## $x
## [1] "IMD Decile"
##
## $y
## [1] "IMD Score"
##
## $caption
## [1] "Solent LSOAs split by Local Authority"
##
## attr(,"class")
## [1] "labels"NB: IMD Decile 10 has LOWER mean IMD score (less deprived)! Which is what it says in the Excel workbook…
So remember: IMD Decile 1 = 10% most deprived; IMD Decile 10 = 10% least deprived
2 Domestic Electricity consumption
2.1 2019
Load LSOA elec data - NB this is all LSOAs not just Solent
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/electricity/LSOA Dom Elec csv/LSOA_ELEC_2019.csv")
domelec_LSOA_2019 <- readr::read_csv(inFile)## Rows: 41727 Columns: 10## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Local Authority Name, Local Authority Code, Middle Layer Super Outp...
## dbl (4): Total number of domestic electricity meters, Total domestic electri...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.domelec_LSOA_2019$LSOA11CD <- domelec_LSOA_2019$`Lower Layer Super Output Area (LSOA) Code`
head(domelec_LSOA_2019)## # A tibble: 6 × 11
## `Local Authority Name` `Local Authority… `Middle Layer Sup… `Middle Layer Sup…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool 001 E02002483
## 2 Hartlepool E06000001 Hartlepool 001 E02002483
## 3 Hartlepool E06000001 Hartlepool 001 E02002483
## 4 Hartlepool E06000001 Hartlepool 001 E02002483
## 5 Hartlepool E06000001 Hartlepool 001 E02002483
## 6 Hartlepool E06000001 Hartlepool 001 E02002483
## # … with 7 more variables: Lower Layer Super Output Area (LSOA) Name <chr>,
## # Lower Layer Super Output Area (LSOA) Code <chr>,
## # Total number of domestic electricity meters <dbl>,
## # Total domestic electricity consumption (kWh) <dbl>,
## # Mean domestic electricity consumption
## (kWh per meter) <dbl>,
## # Median domestic electricity consumption
## (kWh per meter) <dbl>,
## # LSOA11CD <chr>domelec_LSOA_2019$mean_de2019 <- domelec_LSOA_2019$`Mean domestic electricity consumption
(kWh per meter)`2.1.1 Base map
# just add the variables we want
df <- dplyr::select(domelec_LSOA_2019, LSOA11CD, mean_de2019)
lsoa_merged_sf <- merge(lsoa_sf, df) #merging the LSOA boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf) +
geom_sf(aes(fill = mean_de2019)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Electricity (2019, all Solent LSOAs)")
#Mapping it2.1.2 Map with Overlay (leaflet)
# make a new df for use in the map (prevents confusion)
map_df <- merge(lsoa_sf_leaflet, df, by = "LSOA11CD")
qpal <- colorNumeric("Reds", df$mean_de2019, n=9)
leaflet(map_df) %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addPolygons(color = ~qpal(mean_de2019),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))2.1.3 Looking at cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Southampton")
# plotting the electricity consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_de2019)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Portsmouth")
# plotting the electricity consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_de2019)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Winchester")
# plotting the electricity consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_de2019)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Winchester")
2.1.4 Plotting the relationship between Electricty consumption and deprivation
To see if higher electricity use is associated with higher deprivation
domelec_LSOA_2019$la_name <-domelec_LSOA_2019$`Local Authority Name`
solent_elec <- getSolent(domelec_LSOA_2019)
df <- merge(solent_dep, solent_elec, by = "LSOA11CD")
ggplot(df, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y = mean_de2019,
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
labs(x = "IMD Decile",
y = "Electricity use (mean kWh per meter)",
caption = "Solent LSOAs, 2019")
With facets by LA
# or without colour but using facets for Local Authorities
ggplot(df, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y =mean_de2019,
group = IMD_Decile)) +
geom_boxplot() +
labs(x = "IMD Decile",
y = "Electricity use (mean kWh per meter)",
caption = "Solent LSOAs, 2019") +
facet_wrap(la_name.x ~ .)
2.1.5 Difference between 2010 and 2019 dom elec
domelec_LSOA_2010 <- readr::read_csv(paste0(dataFolder,"energy/electricity/LSOA Dom Elec csv/LSOA_ELEC_2010.csv"))## Rows: 41730 Columns: 10## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Local Authority Name, Local Authority Code, Middle Layer Super Outp...
## dbl (4): Total number of domestic electricity meters, Total domestic electri...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.domelec_LSOA_2010$mean_de2010 <- domelec_LSOA_2010$'Mean domestic electricity consumption \n(kWh per meter)'
# create unambiguous lsoa code for matching to each other and the sf geometry
domelec_LSOA_2010$LSOA11CD <- domelec_LSOA_2010$`Lower Layer Super Output Area (LSOA) Code`
# just the vars we want
df <- dplyr::select(domelec_LSOA_2010, LSOA11CD, mean_de2010)
# reuse the 2019 data we already analysed
merged_de <- merge(df, domelec_LSOA_2019, by="LSOA11CD", all=TRUE)
#names(merged_de)
merged_de$mean_de_diff <- as.numeric(merged_de$mean_de2019)-as.numeric(merged_de$mean_de2010)
merged_de$mean_de_diff_pc <- 100*(merged_de$mean_de_diff/merged_de$mean_de2010)
summary(merged_de$mean_de_diff)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5873.3 -711.7 -553.2 -582.8 -411.2 2456.0 3summary(merged_de$mean_de_diff_pc)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -64.93 -17.30 -13.86 -14.03 -10.40 77.31 32.1.5.1 Basic map
# merge to sf geometry on LSOA11CD
lsoa_sf_de20102019<-merge(lsoa_sf, merged_de, by = "LSOA11CD")
ggplot2::ggplot(lsoa_sf_de20102019) +
geom_sf(aes(fill=mean_de_diff))+
scale_fill_continuous(name="Change in Mean consumpton (kWh per meter)",low="green",high="red") +
labs(caption="Solent (all LSOAs)")
t_diff <- dplyr::select(as.data.frame(lsoa_sf_de20102019),
LSOA11CD,
mean_de2010, mean_de2019, mean_de_diff, mean_de_diff_pc)
head(arrange(t_diff, mean_de_diff_pc))## LSOA11CD mean_de2010 mean_de2019 mean_de_diff mean_de_diff_pc
## 1 E01023199 6265.640 3940.512 -2325.128 -37.10919
## 2 E01023201 4715.905 2992.417 -1723.488 -36.54628
## 3 E01022546 5373.513 3416.407 -1957.106 -36.42134
## 4 E01017278 3772.492 2439.883 -1332.610 -35.32438
## 5 E01023224 6119.198 3960.261 -2158.938 -35.28138
## 6 E01022653 5346.714 3549.718 -1796.996 -33.60935head(arrange(t_diff, -mean_de_diff_pc))## LSOA11CD mean_de2010 mean_de2019 mean_de_diff mean_de_diff_pc
## 1 E01017032 3019.755 3296.955 277.2005 9.179569
## 2 E01032842 4437.491 4738.937 301.4457 6.793156
## 3 E01022738 2729.289 2891.538 162.2488 5.944728
## 4 E01022631 6112.007 6375.912 263.9044 4.317803
## 5 E01022489 7585.273 7845.624 260.3506 3.432317
## 6 E01022592 7172.338 7403.228 230.8898 3.219171ggplot2::ggplot(t_diff, aes(x = mean_de_diff_pc)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
2.1.5.2 Change map with Overlay (leaflet)
# merge just the variables we want to the leaflet sf
df <- dplyr::select(merged_de, LSOA11CD, mean_de2010, mean_de2019, mean_de_diff, mean_de_diff_pc)
lsoa_sf_leaflet <- merge(lsoa_sf_leaflet, df) # add on to the
#names(lsoa_sf_leaflet)
qpal <- colorNumeric("Reds", lsoa_sf_leaflet$mean_de_diff, n=9)
# we already checked & transformed the boundaries for leaflet so re-use
leaflet(lsoa_sf_leaflet) %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addPolygons(color = ~qpal(mean_de_diff),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD ),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))2.1.5.3 Cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Southampton")
# plotting the electricity consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill= mean_de_diff)) +
scale_fill_continuous(name = "Change in Mean consumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Southampton")
#BROKEN#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Portsmouth")
# plotting the electricity consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_de_diff)) +
scale_fill_continuous(name = "Change in Mean comsumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Winchester")
# plotting the electricity consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_de_diff)) +
scale_fill_continuous(name = "Change in Mean comsumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Winchester")
2.1.5.4 Plotting the change in dom elec consumption against the deprivation score
df <- dplyr::select(merged_de, LSOA11CD, mean_de_diff, mean_de2010, mean_de2019, mean_de_diff_pc)
LSOAdepelec <- merge(df, LSOAdep, by= "LSOA11CD")
#names(LSOAdepelec)
Solent <- getSolent(LSOAdepelec)
head(Solent)## LSOA11CD mean_de_diff mean_de2010 mean_de2019 mean_de_diff_pc FID
## 1 E01017013 -488.0641 3702.356 3214.292 -13.18253 17179
## 2 E01017014 -678.0286 3991.290 3313.261 -16.98771 17180
## 3 E01017015 -514.1851 3668.720 3154.535 -14.01538 17181
## 4 E01017016 -590.5901 3909.523 3318.933 -15.10645 17182
## 5 E01017017 -429.7497 4101.945 3672.195 -10.47673 17183
## 6 E01017018 -682.7941 4079.974 3397.180 -16.73526 17184
## lsoa11cd lsoa11nm lsoa11nmw st_areasha st_lengths IMD_Rank
## 1 E01017013 Portsmouth 012A Portsmouth 012A 145126.5 1856.920 12407
## 2 E01017014 Portsmouth 008A Portsmouth 008A 757386.9 5206.517 15332
## 3 E01017015 Portsmouth 014A Portsmouth 014A 472645.1 5215.706 14477
## 4 E01017016 Portsmouth 014B Portsmouth 014B 122926.9 2668.623 14960
## 5 E01017017 Portsmouth 014C Portsmouth 014C 166916.5 2596.481 21428
## 6 E01017018 Portsmouth 014D Portsmouth 014D 184942.3 2582.152 16262
## IMD_Decile LSOA01NM LADcd LADnm IMDScore IMDRank0 IMDDec0
## 1 4 Portsmouth 012A E06000044 Portsmouth 22.464 12407 4
## 2 5 Portsmouth 008A E06000044 Portsmouth 18.892 15332 5
## 3 5 Portsmouth 014A E06000044 Portsmouth 19.845 14477 5
## 4 5 Portsmouth 014B E06000044 Portsmouth 19.349 14960 5
## 5 7 Portsmouth 014C E06000044 Portsmouth 12.658 21428 7
## 6 5 Portsmouth 014D E06000044 Portsmouth 17.856 16262 5
## IncScore IncRank IncDec EmpScore EmpRank EmpDec EduScore EduRank EduDec
## 1 0.106 15469 5 0.068 19320 6 30.333 8420 3
## 2 0.066 22396 7 0.055 23101 8 24.099 11292 4
## 3 0.094 17234 6 0.076 17293 6 18.252 14957 5
## 4 0.077 20105 7 0.052 23906 8 28.237 9297 3
## 5 0.064 22802 7 0.064 20459 7 11.811 20132 7
## 6 0.068 21959 7 0.059 21781 7 20.051 13731 5
## HDDScore HDDRank HDDDec CriScore CriRank CriDec BHSScore BHSRank BHSDec
## 1 0.165 13549 5 0.360 11020 4 12.194 26353 9
## 2 0.064 15026 5 0.626 7377 3 14.358 23812 8
## 3 0.340 11096 4 0.656 7011 3 16.157 21470 7
## 4 -0.213 19329 6 0.327 11537 4 15.403 22475 7
## 5 -0.405 22178 7 -0.165 19129 6 13.252 25110 8
## 6 -0.235 19645 6 0.573 8092 3 10.881 27759 9
## EnvScore EnvRank EnvDec IDCScore IDCRank IDCDec IDOScore IDORank IDODec
## 1 54.467 1248 1 0.157 13404 5 0.123 17028 6
## 2 50.479 1835 1 0.122 16881 6 0.067 25852 8
## 3 35.575 5984 2 0.133 15661 5 0.109 18806 6
## 4 57.087 950 1 0.100 19443 6 0.130 16167 5
## 5 38.706 4718 2 0.102 19243 6 0.065 26124 8
## 6 53.361 1384 1 0.094 20274 7 0.093 21058 7
## CYPScore CYPRank CYPDec ASScore ASRank ASDec GBScore GBRank GBDec WBScore
## 1 0.937 4233 2 0.295 16557 6 -1.062 30234 10 0.655
## 2 0.529 8487 3 0.310 14844 5 -0.474 23667 8 0.507
## 3 0.115 14238 5 0.309 15009 5 -0.372 22080 7 0.746
## 4 0.827 5209 2 0.296 16424 6 -0.592 25422 8 0.939
## 5 -0.291 20831 7 0.281 18217 6 -0.501 24084 8 0.273
## 6 0.300 11444 4 0.301 15879 5 -1.532 32176 10 0.507
## WBRank WBDec IndScore IndRank IndDec OutScore OutRank OutDec TotPop DepChi
## 1 12076 4 1.202 2441 1 1.146 3244 1 1789 463
## 2 12755 4 1.035 3608 2 1.248 2694 1 1635 328
## 3 11640 4 0.731 6461 2 0.638 7063 3 1496 291
## 4 10792 4 1.189 2521 1 1.389 2041 1 1589 341
## 5 13838 5 0.663 7167 3 1.069 3668 2 1433 227
## 6 12753 4 1.082 3248 1 1.366 2156 1 1600 290
## Pop16_59 Pop60_ WorkPop Shape__Area Shape__Length la_name
## 1 1106 220 1096.75 363247.0 2939.704 Portsmouth
## 2 991 316 972.75 1895866.7 8234.341 Portsmouth
## 3 872 333 860.25 1182876.9 8248.480 Portsmouth
## 4 973 275 980.25 307650.4 4223.008 Portsmouth
## 5 852 354 852.00 417678.9 4105.026 Portsmouth
## 6 930 380 925.25 462807.6 4085.935 Portsmouthggplot(Solent, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y = mean_de_diff_pc,
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
labs(x = "IMD Decile",
y = "% change in mean kWh per meter",
caption = "Electricity 2010-2019, all Solent LSOAs")
Try adding facets for each Local Authority
# or without colour but using facets for Local Authorities
ggplot(Solent, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y =mean_de_diff_pc,
group = IMD_Decile)) +
geom_boxplot() +
labs(x = "IMD Decile",
y = "% change in mean kWh per meter",
caption = "Electricity 2010-2019, all Solent LSOAs") +
facet_wrap(la_name ~ .)
nrow(Solent)## [1] 11363 Mapping Gas
3.1 2019
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/gas/LSOA Gas csv/LSOA_GAS_2019.csv")
domgas_LSOA_2019 <- readr::read_csv(inFile)## Rows: 40213 Columns: 11## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Local Authority Name, Local Authority Code, MSOA Name, Middle Layer...
## dbl (4): Number of consuming meters, Consumption (kWh), Mean consumption (kW...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.domgas_LSOA_2019$LSOA11CD <- domgas_LSOA_2019$`Lower Layer Super Output Area (LSOA) Code`
domgas_LSOA_2019$mean_dg2019 <- domgas_LSOA_2019$`Mean consumption (kWh per meter)`
head(domgas_LSOA_2019)## # A tibble: 6 × 13
## `Local Authority… `Local Authority… `MSOA Name` `Middle Layer Sup… `LSOA Name`
## <chr> <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 2 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 3 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 4 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 5 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 6 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## # … with 8 more variables: Lower Layer Super Output Area (LSOA) Code <chr>,
## # Number of consuming meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>,
## # Number of non-consuming meters <chr>, LSOA11CD <chr>, mean_dg2019 <dbl>3.1.1 Basic map
df <- dplyr::select(domgas_LSOA_2019, LSOA11CD, mean_dg2019)
lsoa_merged_sf_gas <- merge(lsoa_sf, df) #merging the boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf_gas) +
geom_sf(aes(fill = mean_dg2019)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all LSOAs)")
#Mapping itAha - this one shows where there is no gas :-)
3.1.2 Map with Overlay
# re-use the df from previous chunk
lsoa_sf_leaflet <- merge(lsoa_sf_leaflet, df, by = "LSOA11CD")
qpal <- colorNumeric("Reds", lsoa_sf_leaflet$mean_dg2019, n=9)
leaflet(lsoa_sf_leaflet) %>%
addTiles() %>%
addPolygons(color= ~qpal(mean_dg2019),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight=highlightOptions(
weight=5,
color="#666",
fillOpacity = 0.7,
bringToFront = TRUE))3.1.3 Cities in More detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Southampton")
# plotting the gas consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_dg2019)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Portsmouth")
# plotting the gas consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_dg2019)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Winchester")
# plotting the gas consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = mean_dg2019)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Winchester")
3.1.4 Plotting gas usage against deprivation
domgas_LSOA_2019$la_name <-domgas_LSOA_2019$`Local Authority Name`
solent_gas <- getSolent(domgas_LSOA_2019)
df <- merge(solent_dep, solent_gas, by = "LSOA11CD")
ggplot(df, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y = mean_dg2019,
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
labs(x = "IMD Decile",
y = "Gas Consumption")
Much more of a correlation with low deprivation (higher deciles)
# or without colour but using facets for Local Authorities
ggplot(df, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y =mean_dg2019,
group = IMD_Decile)) +
geom_boxplot() +
labs(x = "IMD Decile",
y = "Gas Consumption") +
facet_wrap(la_name.y ~ .)
nrow(Solent)## [1] 11363.1.5 Difference between 2010 and 2019 dom gas
domgas_LSOA_2010 <- readr::read_csv(paste0(dataFolder, "energy/gas/LSOA Gas csv/LSOA_GAS_2010.csv"))## Rows: 40122 Columns: 10## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Local Authority Name, Local Authority Code, MSOA Name, Middle Layer...
## dbl (4): Number of meters, Consumption (kWh), Mean consumption (kWh per mete...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.domgas_LSOA_2010$mean_dg2010 <- domgas_LSOA_2010$'Mean consumption (kWh per meter)'
domgas_LSOA_2010$LSOA11CD <- domgas_LSOA_2010$`Lower Layer Super Output Area (LSOA) Code`
df <- dplyr::select(domgas_LSOA_2010, LSOA11CD, mean_dg2010) # just the vars we need
merged_dgas <- merge(domgas_LSOA_2019, df, by = "LSOA11CD", all = TRUE)
#names(merged_dgas)
merged_dgas$dg_diff<-as.numeric(merged_dgas$mean_dg2019) - merged_dgas$mean_dg2010
summary(merged_dgas)## LSOA11CD Local Authority Name Local Authority Code
## Length:40226 Length:40226 Length:40226
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## MSOA Name Middle Layer Super Output Area (MSOA) Code
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## LSOA Name Lower Layer Super Output Area (LSOA) Code
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Number of consuming meters Consumption (kWh)
## Min. : 5.0 Min. :1.860e+04
## 1st Qu.: 492.0 1st Qu.:6.166e+06
## Median : 615.0 Median :8.062e+06
## Mean : 599.2 Mean :8.087e+06
## 3rd Qu.: 705.0 3rd Qu.:9.839e+06
## Max. :87991.0 Max. :1.018e+09
## NA's :13 NA's :13
## Mean consumption (kWh per meter) Median consumption (kWh per meter)
## Min. : 1193 Min. : 537.1
## 1st Qu.:11476 1st Qu.:10361.9
## Median :13197 Median :12045.5
## Mean :13768 Mean :12531.2
## 3rd Qu.:15384 3rd Qu.:14090.4
## Max. :47003 Max. :49456.0
## NA's :13 NA's :13
## Number of non-consuming meters mean_dg2019 la_name
## Length:40226 Min. : 1193 Length:40226
## Class :character 1st Qu.:11476 Class :character
## Mode :character Median :13197 Mode :character
## Mean :13768
## 3rd Qu.:15384
## Max. :47003
## NA's :13
## mean_dg2010 dg_diff
## Min. : 553.6 Min. :-22666
## 1st Qu.:12997.8 1st Qu.: -2090
## Median :14915.0 Median : -1601
## Mean :15444.4 Mean : -1676
## 3rd Qu.:17241.8 3rd Qu.: -1143
## Max. :52729.2 Max. : 23242
## NA's :104 NA's :1173.1.5.1 Plotting the change in gas usage against the deprivation
LSOAdepgas <- merge(merged_dgas, LSOAdep, by= "LSOA11CD")
#names(LSOAdepgas)
df <- dplyr::select(LSOAdepgas, LSOA11CD, la_name.x, mean_dg2019, IMDScore, mean_dg2010, IMD_Decile,dg_diff
)
head(df)## LSOA11CD la_name.x mean_dg2019 IMDScore mean_dg2010 IMD_Decile
## 1 E01000001 City of London 11084.444 6.208 13298.600 9
## 2 E01000002 City of London 25129.528 5.143 13216.867 10
## 3 E01000003 City of London 7870.607 19.402 8973.772 5
## 4 E01000005 City of London 6955.060 28.652 7836.029 3
## 5 E01000006 Barking and Dagenham 16068.696 19.837 16774.027 5
## 6 E01000007 Barking and Dagenham 8118.158 31.576 9469.377 3
## dg_diff
## 1 -2214.1563
## 2 11912.6609
## 3 -1103.1645
## 4 -880.9685
## 5 -705.3315
## 6 -1351.2193df$la_name <- df$la_name.x
Solent <- getSolent(df)
head(Solent)## LSOA11CD la_name.x mean_dg2019 IMDScore mean_dg2010 IMD_Decile dg_diff
## 1 E01017013 Portsmouth 10222.49 22.464 12059.10 4 -1836.611
## 2 E01017014 Portsmouth 11336.27 18.892 13059.83 5 -1723.558
## 3 E01017015 Portsmouth 10204.14 19.845 11795.50 5 -1591.360
## 4 E01017016 Portsmouth 10837.64 19.349 12394.83 5 -1557.193
## 5 E01017017 Portsmouth 11435.73 12.658 13123.21 7 -1687.480
## 6 E01017018 Portsmouth 11628.42 17.856 13311.45 5 -1683.026
## la_name
## 1 Portsmouth
## 2 Portsmouth
## 3 Portsmouth
## 4 Portsmouth
## 5 Portsmouth
## 6 Portsmouthggplot(Solent, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y =100*(dg_diff/mean_dg2010),
group = IMD_Decile,
colour = IMD_Decile)) +
geom_boxplot() +
scale_color_continuous(name = "IMD Decile") + # rename the legend scale
labs(x = "IMD Decile",
y = "% change",
caption = "Mean gas kWh 2010-2019, all Solent LSOas")## Warning: Removed 5 rows containing non-finite values (stat_boxplot).
Try adding facets for each Local Authority and remove the outlier
# or without colour but using facets for Local Authorities
Solentex <- filter(Solent,100*(dg_diff/mean_dg2010)<50)
ggplot(Solentex, aes(x = as.factor(IMD_Decile), # forces a box plot at each value
y =100*(dg_diff/mean_dg2010),
group = IMD_Decile)) +
geom_boxplot() +
labs(x = "IMD Decile",
y = "% change",
caption = "Mean gas kWh 2010-2019, all Solent LSOas") +
facet_wrap(la_name ~ .)
nrow(Solent)## [1] 11083.1.5.2 Basic change map
#Green indicates a decrease in gas consumption from 2010 to 2019
#Red indicates an increase in gas consumption from 2010 to 2019
df <- dplyr::select(merged_dgas, LSOA11CD, mean_dg2010, mean_dg2019, dg_diff)
lsoa_dgas20102019 <- merge(lsoa_sf, df, by = "LSOA11CD") #merging these
ggplot2::ggplot(lsoa_dgas20102019) + geom_sf(aes(fill=dg_diff))+
scale_fill_continuous(name="Change in Gas: Mean kWh per meter", low="green",high="red")+
labs(caption ="Solent (all MSOAs)") ##BROKEN
##Need to change the varibale names to reflect what is actually there
lsoa_dgas20102019$pc_diff <- 100*(lsoa_dgas20102019$dg_diff/lsoa_dgas20102019$mean_dg2010)
t_diff <- dplyr::select(as.data.frame(lsoa_dgas20102019),
LSOA11CD, LSOA11NM,
mean_dg2010, mean_dg2019, dg_diff, pc_diff)
head(arrange(t_diff, pc_diff))## LSOA11CD LSOA11NM mean_dg2010 mean_dg2019 dg_diff pc_diff
## 1 E01032746 Southampton 029F 12086.10 5619.771 -6466.329 -53.50220
## 2 E01023224 Winchester 014A 14088.72 8538.244 -5550.481 -39.39662
## 3 E01032745 Southampton 029E 11305.62 6991.681 -4313.935 -38.15745
## 4 E01023155 Test Valley 003A 15019.06 9591.014 -5428.046 -36.14105
## 5 E01023199 Test Valley 013D 15604.59 10074.671 -5529.917 -35.43776
## 6 E01023225 Winchester 004A 15339.93 10214.353 -5125.581 -33.41332head(arrange(t_diff, -pc_diff))## LSOA11CD LSOA11NM mean_dg2010 mean_dg2019 dg_diff pc_diff
## 1 E01017281 Southampton 032D 4264.534 8429.059 4164.5247 97.654849
## 2 E01032755 Southampton 029I 4501.097 5207.299 706.2018 15.689547
## 3 E01017202 Southampton 016C 8637.537 9876.979 1239.4416 14.349480
## 4 E01017140 Southampton 023D 10685.891 11672.833 986.9429 9.235944
## 5 E01017114 Portsmouth 004C 7523.706 8158.664 634.9574 8.439423
## 6 E01017207 Southampton 015A 7734.902 8152.091 417.1893 5.393595ggplot2::ggplot(t_diff, aes(x = pc_diff)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 5 rows containing non-finite values (stat_bin).
3.1.5.3 Change map with overlay
Using leaflet
lsoa_sf_leaflet <- merge(lsoa_sf_leaflet, t_diff, by = "LSOA11CD")
qpal <- colorNumeric("Reds", lsoa_sf_leaflet$dg_diff, n=9)
leaflet(lsoa_sf_leaflet) %>%
addTiles() %>%
addPolygons(color = ~qpal(dg_diff),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))3.1.5.4 Cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Southampton")
# plotting the gas consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "2010-2019, Southampton LSOAs")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Portsmouth")
# plotting the gas consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "2010-2019, Portsmouth LSOAs")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Winchester")
# plotting the gas consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "2010-2019, Winchester LSOAs")